AI operates in two primary environments: on-device and cloud-based. On-device AI processes data locally, ensuring privacy and speed by eliminating the need for internet connectivity. Cloud-based AI, on the other hand, leverages powerful remote servers to handle complex computations and large-scale data analysis, enabling more robust capabilities but often at the cost of latency and potential privacy concerns. 

Apple Intelligence exemplifies the strengths of on-device AI, with innovations like Siri, Face ID, and real-time photo enhancements all designed to prioritise user privacy while delivering seamless, responsive experiences.  Unlike cloud-based AI, which may send sensitive data to external servers for processing, Apple’s approach ensures that personal information stays on the user’s device and is protected by advanced encryption. This difference builds trust and empowers users with faster, more reliable interactions. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Karel Callens, CEO at Luzmo, about best practices for developers integrating AI into their products. 

Key Takeaways:

• Apple Intelligence operates on-device but can access cloud resources.
• Developers must implement robust security measures for user data.
• Clear data policies enhance user trust in AI solutions.
• Opt-in and opt-out options empower users regarding their data.
• Education on AI usage is crucial for consumer confidence.
• Shared security standards can mitigate AI misuse.
• Regulation is necessary to keep pace with AI advancements.
• Big tech companies have a responsibility to ensure ethical AI use.

Chapters:

00:00 - Introduction to AI: On-Device vs Cloud-Based

02:54 - Understanding Apple Intelligence and Its Benefits

05:47 - Security Measures in AI Integration

09:03 - Building Trust Through Transparency and Regulation

11:50 - Best Practices for Developers in AI Implementation

15:04 - The Role of Education in AI Trust and Security

17:47 - The Future of AI: Regulation and Responsibility

AI Personas are the cornerstone of how these systems interact with users, delivering tailored and engaging experiences. These personas—crafted from user research, behavioural insights, and cultural contexts—help define an AI's tone, style, and decision-making approach. Whether it’s a friendly virtual assistant or a professional customer service bot, personas ensure that AI systems resonate with their audiences while maintaining a consistent identity. 

However, developing personas for AI isn’t without its challenges. Ensuring that AI responses remain appropriate, ethical, and unbiased while preserving a unique persona requires careful consideration. From avoiding stereotypes to addressing edge cases, the process demands robust testing and a clear understanding of how diverse user interactions can unfold. 

When personas fail to account for the complexity of real-world scenarios, the risk of inappropriate or harmful responses increases. By combining creative storytelling with ethical AI design principles, organisations can navigate these challenges and build AI systems that are engaging and responsible in their behaviour.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Cobus Greyling, Chief Evangelist at Kore.ai, about the influence of cultural norms and value systems on AI and strategies for maintaining control over AI behaviour. 

Key Takeaways: 

• Personas in AI shape user interactions and trust.
• Cultural norms influence AI decision-making processes.
• Balancing control and agency is crucial for effective AI.
• Adversarial attacks can undermine AI reliability.
• Transparency is essential for user confidence in AI.
• Organisations should not offload too much responsibility to AI.
• AI should enhance human creativity, not replace it.
• Proof of value is necessary for AI technology implementation.

Chapters:

00:00 - Introduction to AI Personas and Their Impact

02:34 - The Role of Personas in AI Behavior

05:51 - Challenges in Ensuring Appropriate AI Responses

09:07 - Cultural Norms and Value Systems in AI

10:30 - Balancing Control and Agency in AI

14:14 - Strategies for Maintaining Control Over AI Behavior

21:24 - The Importance of Responsibility in AI Usage

Low-code and no-code platforms are revolutionising application development by empowering technical and non-technical users to quickly and efficiently build powerful applications. These platforms provide intuitive visual interfaces and pre-built templates that enable users to create complex workflows, automate processes, and deploy applications without writing extensive lines of code. 

By simplifying development, low-code and no-code tools open up software creation to a wider range of contributors, from professional developers looking to accelerate delivery times to business users aiming to solve specific problems independently. This democratisation of development reduces the demand for IT resources and fosters a culture of innovation and agility within organisations.

The impact of low-code and no-code technology extends beyond just speed and accessibility; it’s transforming how businesses adapt to change and scale their digital solutions. These platforms allow companies to quickly respond to evolving customer needs, regulatory requirements, and competitive pressures without the lengthy timelines associated with traditional development cycles. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Michael West, Analyst at Lionfish Tech Advisors about LCNC platforms and their benefits. 

Key Takeaways:

• Low-code and no-code platforms enable business solutions without coding.
• These platforms broaden the developer base to include non-technical users.
• Choosing the right platform involves considering functionality, standards, and vendor viability.
• Low-code platforms can handle enterprise-level applications effectively.
• AI integration is transforming how applications are developed.
• Democratisation of development addresses the shortage of professional developers.
• The market for low-code and no-code platforms is rapidly evolving.
• Future trends will focus on AI capabilities and user experience.

Chapters: 

00:00 Introduction to Low-Code and No-Code Platforms

02:59 The Evolution of Development Roles

05:49 Key Considerations for Adopting LCNC Tools

09:04 Democratizing Development and Innovation

11:59 Future Trends in Low-Code and No-Code Markets

The intersection of cryptography and GPU programming has changed the face of secure data processing, making methods for encryption and decryption much faster and more efficient than ever imagined. Cryptography is the science of encrypting data with intricate algorithms initially designed to operate on very intensive computational powers. GPU programming provides the ability to utilise parallel processing of graphics processing units in cryptographic processes so they perform with unmatched speed. 

While continuously evolving, GPUs are furnishing the computational muscle to execute ever-higher-level cryptographic algorithms without performance penalties. Developers now fully avail of the power of GPU parallelism to perform several thousand encryption tasks simultaneously, which is difficult for traditional CPUs to keep up with. This efficiency is critical in this growing data and rising cyber threat era, where organisations need rapid encryption and robust security. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Agnes Leroy, Senior Software Engineer at Zama, about the significance of encryption in high-stakes industries, the role of women in tech and the importance of mentorship in overcoming barriers in the industry.

Key Takeaways: 

• GPUs have evolved from graphics rendering to critical roles in data security.
• Fully homomorphic encryption allows computations on encrypted data.
• Quantum-resistant methods are crucial for future-proofing encryption.
• High-stakes industries require robust encryption to protect sensitive data.
• Diverse environments in tech foster innovation and collaboration.
• The future of encryption technology is exciting and unpredictable.

Chapters: 

00:00 - Introduction to Cryptography and GPU Programming

01:08 - The Evolution of GPUs in Data Security

03:33 - Challenges in Traditional vs Modern Encryption

05:50 - Quantum Resistance in Encryption Techniques

07:40 - The Future of GPUs in Data Privacy

08:38 - Importance of Encryption in High-Stakes Industries

10:00 - Potential Applications of Fully Homomorphic Encryption

11:42 - Women in Tech: Overcoming Barriers

15:33 - Conclusion and Resources

LLMs and AI have increasingly become major contributors to transforming content creation today. Understanding and using prompt skills appropriately can help organisations optimise AI to generate high-quality content efficiently. 

While AI offers multiple benefits, it's important to acknowledge the potential risks associated with its implementation. Organisations are advised to carefully consider factors such as data privacy, bias, and the ethical implications of AI-generated content. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Prof. Yash Shreshta, Assistant Professor at the University of Laussane, about prompt engineering and its benefits. 

Key Takeaways

• Prompts are commands given to AI to perform tasks.
• Prompt engineering allows users to communicate effectively with AI.
• Iterative processes improve the quality of AI outputs.
• Understanding LLM limitations is crucial for effective use.
• Collaboration can enhance the creative process with AI.
• The role of prompt engineering is rapidly evolving with AI advancements.
• Data privacy is a significant risk when using LLMs.
• Over-reliance on AI can lead to skill degradation.
• Organisations should integrate human creativity with AI.
• Regular training on prompt engineering is essential for maximising LLM benefits.

Chapters

00:00 Introduction to Prompt Engineering and AI

01:30 Understanding Prompt Engineering

04:15 The Importance of Prompt Engineering Skills

06:37 Best Practices for Effective Prompts

08:31 The Evolving Role of Prompt Engineering

11:20 Risks and Challenges of AI in Organizations

13:15 The Future of Creativity with AI

The fraud division has witnessed a dramatic transformation in the age of artificial intelligence (AI). As technology advances, so do the methods employed by fraudsters. Modern criminals use sophisticated techniques, such as deep learning and natural language processing, to deceive individuals and organisations alike. Such techniques allow them to mimic human behaviour, manipulate data, and exploit vulnerabilities in security systems.

That’s why organisations are embracing AI's strengths to combat these evolving threats. AI-driven solutions can provide real-time detection of fraudulent activities, analyse vast amounts of data to identify patterns and anomalies, and automate response processes. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Xavier Sheikrojan, Senior Risk Intelligence Manager at Signifyd, about AI fraud. 

Key Takeaways:

• Fraudsters have evolved from individuals to organised criminal enterprises.
• AI enables fraudsters to scale their attacks rapidly and strategically.
• Phishing attacks have become more sophisticated with AI-generated content.
• Synthetic identities can be created easily, complicating fraud prevention.
• Opportunistic fraud is impulsive, while proactive fraud is well-planned.
• Businesses often fail to act post-breach due to resource constraints.
• Inaction after a breach can lead to repeated attacks by fraudsters.
• AI must be used to combat AI-driven fraud effectively.
• Balancing fraud detection with customer experience is crucial for businesses. 

Chapters:

00:00 Introduction to AI and Fraud

01:32 The Evolution of Cybercrime

05:43 AI's Role in Modern Fraud Techniques

09:55 Opportunistic vs. Proactive Fraud

12:44 Business Inaction and Its Consequences

15:59 Combating AI-Driven Fraud with AI

As AI technologies become more integrated into business operations, they bring opportunities and challenges. AI’s ability to process vast amounts of data can enhance decision-making but also raise concerns about data privacy, security, and regulatory compliance. 

Ensuring that AI-driven systems adhere to data protection laws, such as GDPR and CCPA, is critical to avoid breaches and penalties. Balancing innovation with strict compliance and robust data security measures is essential as organisations explore AI’s potential while protecting sensitive information.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Erin Nicholson, Global Head of Data Protection and AI Compliance at Thoughtworks, about the importance of compliance frameworks, best practices for transparency and accountability, and the need for collaboration among various teams to build trust in AI systems.

Key Takeaways:

• AI systems are powerful but require ethical and compliant design.
• Lack of standardisation in AI regulations poses significant challenges.
• AI models often need help with explainability and transparency.
• Compliance frameworks are essential for implementing AI in critical sectors.
• Documentation and audits are crucial for maintaining AI accountability.
• Baselining pre-AI processes helps build public trust in AI systems.
• Organisations should map regulations to the most stringent standards.
• Cross-functional collaboration is vital for effective AI compliance.

Chapters: 

00:00 - Introduction to AI, Data Protection, and Compliance

02:08 - Challenges in AI Implementation and Compliance

05:56 - The Role of Compliance Frameworks in Critical Sectors

10:31 - Best Practices for Transparency and Accountability in AI

14:48 - Navigating Regional Regulations for AI Compliance

17:43 - Collaboration for Trustworthiness in AI Systems

As organisations increasingly migrate to cloud environments, they face a critical challenge: ensuring the security and privacy of their data. 

Cloud technologies offer many benefits, including scalability, cost savings, and flexibility. However, they also introduce new risks, such as potential data breaches, unauthorised access, and compliance issues. 

With sensitive data stored and processed off-premises, maintaining control and visibility over that data becomes more complex. As cyber threats continue to evolve, robust data protection strategies are essential to safeguarding information in the cloud.

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Sergei Serdyuk, VP of Product Management at NAKIVO, about the factors driving cloud adoption, the importance of having a robust disaster recovery plan, best practices for data protection, and the challenges of ensuring compliance with regulations.

Key Takeaways: 

• Cloud adoption is accelerated by low barriers to entry.
• Scalability in cloud environments is easier than on-premises.
• Data in the cloud is vulnerable and needs protection.
• The shared responsibility model places data protection on the user.
• A comprehensive disaster recovery plan is crucial for businesses.
• Regular testing of disaster recovery plans is essential.
• Data protection strategies must include regular reviews and updates.
• Compliance with data protection regulations is complex and varies by region.
• Balancing security and operational efficiency is a key challenge.

Chapters: 

00:00 - Introduction to Cloud Technologies and Data Protection

01:26 - Factors Accelerating Cloud Adoption

03:48 - The Importance of Data Protection in the Cloud

06:39 - Developing a Comprehensive Disaster Recovery Plan

10:05 - Best Practices for Data Protection

13:31 - Ensuring Compliance in Cloud Environments

15:56 - The Role of Continuous Monitoring in Data Protection

18:19 - Balancing Security and Operational Efficiency

Managed Service Providers (MSPs) are evolving beyond traditional IT support, becoming strategic partners in driving business growth. By embracing AI technologies, MSPs are improving operational efficiency, streamlining service delivery, and offering smarter solutions to meet modern challenges.

As businesses navigate digital transformation, MSPs are crucial in optimising IT infrastructure, enhancing security, and providing tailored solutions that fuel innovation. With AI-powered tools, MSPs meet today's demands and help businesses stay competitive. 

In this episode, Paulina Rios Maya, Head of Industry Relations, speaks to Jason Kemsley, Co-founder and CRO of Uptime, about the proactive strategies that MSPs can adopt using AI, the challenges they face in implementation, and the ethical considerations surrounding AI solutions.

Key Takeaways:

• MSPs are transitioning from traditional IT support to strategic partners.
• AI is enhancing operational efficiency but not replacing human roles.
• Proactive support is often neglected due to resource constraints.
• AI can help MSPs predict and prevent IT issues.
• Challenges in adopting AI include unrealistic expectations and lack of accountability.
• There is potential for MSPs to develop their own AI tools.

Chapters:

00:00 - Introduction to Managed Service Providers (MSPs)

02:03 - The Evolving Role of MSPs in Business Growth

04:00 - AI's Impact on Service Delivery Models

07:22 - Proactive Support Strategies with AI

10:16 - Challenges in Adopting AI for MSPs

12:40 - Ethics and Accountability in AI Solutions

Trusted AI ensures that people, data, and AI systems work together transparently to create real value. This requires a focus on performance, innovation, and cost-effectiveness, all while maintaining transparency. However, challenges such as misaligned business strategies and data readiness can undermine trust in AI systems. 

To build trusted AI, it’s crucial to first trust the data. A robust data platform is essential for creating reliable and sustainable AI systems. Tools like Teradata’s ClearScape Analytics help address concerns about AI, including issues like generative AI hallucinations, by providing a solid foundation of trusted data and an open, connected architecture.

In this episode, Doug Laney, Analytics Strategy Innovation Fellow with West Monroe Partners, speaks to Vedat Akgun, VP of Data Science & AI and Steve Anderson, Senior Director of Data Science & AI at Teradata, about trusted AI. 

Key Takeaways:

• Value creation, performance, innovation, and cost-effectiveness are crucial for achieving trusted AI.
• Trusting data is essential before building AI capabilities to avoid biases, inaccuracies, and ethical violations.
• A robust data platform is a foundation for creating trusted and sustainable AI systems.
• Generative AI raises concerns about hallucinations and fictitious data, highlighting the need for transparency and accountability.
• Teradata offers features and capabilities, such as ClearScape Analytics and an open and connected architecture, to address trust issues in AI.

Chapters:

00:00 - Introduction and Defining Trusted AI

01:33 - Value Creation and the Importance of Driving Business Value

03:27 - Transparency as a Principle of Trusted AI

09:00 - Trusting Data Before Building AI Capabilities

14:51 - The Role of a Robust Data Platform in Trusted AI

21:09 - Concerns about Trust in Generative AI

23:03 - Addressing Trust Issues with Teradata's Features and Capabilities

25:01 - Conclusion